
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase    :   List[Any]						=			argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase    :   Union[str, Any]						=			parser.parse_args()

		lowercase    :   Tuple						=			UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase    :   Tuple						=			CLIPImageProcessor()
		lowercase    :   List[str]						=			CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase    :   Tuple						=			UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)

import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
		import torch


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowercase    :   Any						=			logging.get_logger(__name__)
lowercase    :   Union[str, Any]						=			{'vocab_file': 'spiece.model'}

lowercase    :   Tuple						=			{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

lowercase    :   List[str]						=			{
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	VOCAB_FILES_NAMES
	_A								=	PRETRAINED_VOCAB_FILES_MAP
	_A								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=	['input_ids', 'attention_mask']







	def __init__(   self						:int	,  a						:List[Any]	,  a						:int=False	,  a						:int=False	,  a						:List[str]=False	,  a						:Optional[Any]=None	,  a						:Tuple=None	,  a						:Optional[Any]=None	,  a						:Any=None	,  a						:Optional[Dict[str, Any]] = None	,  **a						:Dict	,  )						->					None:
								__UpperCamelCase      :  List[str]       =   {} if sp_model_kwargs is None else sp_model_kwargs

								__UpperCamelCase      :  Any       =   kwargs.get("name_or_path"		)
								if name_or_path is None:
															logger.warning(
															    "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
															    " you are testing the model, this can safely be ignored"		)
															__UpperCamelCase      :  Any       =   "None"

								# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
								__UpperCamelCase      :  Union[str, Any]       =   "<|endoftext|>" if eos_token is None else eos_token
								__UpperCamelCase      :  int       =   "<unk>" if unk_token is None else unk_token
								if "gpt-sw3-7b" in name_or_path:
															__UpperCamelCase      :  str       =   unk_token if pad_token is None else pad_token
															__UpperCamelCase      :  Dict       =   eos_token if bos_token is None else bos_token
								else:
															__UpperCamelCase      :  Optional[int]       =   "<pad>" if pad_token is None else pad_token
															__UpperCamelCase      :  Any       =   "<s>" if bos_token is None else bos_token

								super().__init__(
								    do_lower_case=a	,  remove_space=a	,  keep_accents=a	,  bos_token=a	,  eos_token=a	,  unk_token=a	,  pad_token=a	,  sp_model_kwargs=self.sp_model_kwargs	,  **a	,  )

								__UpperCamelCase      :  List[str]       =   do_lower_case
								__UpperCamelCase      :  List[str]       =   remove_space
								__UpperCamelCase      :  int       =   keep_accents
								__UpperCamelCase      :  List[Any]       =   vocab_file

								__UpperCamelCase      :  Optional[Any]       =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(a		)

								# Used for whitespace normalization in input texts
								# fmt : off
								__UpperCamelCase      :  Dict       =   {" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
								# fmt : on

								# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
								__UpperCamelCase      :  int       =   re.compile(
								    f'[{"".join(map(a	,  list(range(0	,  9		)		) + list(range(1_1	,  3_2		)		) + list(range(1_2_7	,  1_6_0		)		) + [1_6_0, 1_7_3, 8_2_0_3]		)		)}]'		)







	def __getstate__(   self						:Tuple		)						->					Union[str, Any]:
								__UpperCamelCase      :  int       =   self.__dict__.copy()
								__UpperCamelCase      :  Optional[Any]       =   None
								return state







	def __setstate__(   self						:Union[str, Any]	,  a						:Optional[int]		)						->					Tuple:
								__UpperCamelCase      :  int       =   d

								# for backward compatibility
								if not hasattr(self	,  "sp_model_kwargs"		):
															__UpperCamelCase      :  Optional[int]       =   {}

								__UpperCamelCase      :  Dict       =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	@property
	# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
	def   _lowerCamelCase							(   self						:int		)						->					int:
								return len(self.sp_model		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					str:
								__UpperCamelCase      :  int       =   self.non_printing_characters_re.sub(""	,  a		)

								# Normalize whitespaces
								__UpperCamelCase      :  Any       =   "".join([char if char not in self.whitespaces else " " for char in text]		)

								# NFC Unicode normalization
								__UpperCamelCase      :  str       =   unicodedata.normalize("NFC"	,  a		)
								return text







	def   _lowerCamelCase							(   self						:int	,  a						:str	,  **a						:str		)						->					List[str]:
								__UpperCamelCase      :  Any       =   self.preprocess_text(a		)
								return self.sp_model.encode(a	,  out_type=a		)







	def   _lowerCamelCase							(   self						:int	,  a						:str		)						->					int:
								return self.sp_model.PieceToId(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:int		)						->					str:
								return self.sp_model.IdToPiece(a		)







	@staticmethod
	def   _lowerCamelCase							(   a						:str		)						->					str:
								return out_string







	def   _lowerCamelCase							(   self						:Optional[int]	,  a						:List[str]		)						->					str:
								__UpperCamelCase      :  List[Any]       =   []
								__UpperCamelCase      :  Tuple       =   ""
								__UpperCamelCase      :  Optional[Any]       =   False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																						if not prev_is_special:
																													out_string += " "

																						out_string += self.sp_model.decode(a		) + token
																						__UpperCamelCase      :  int       =   True
																						__UpperCamelCase      :  Any       =   []
															else:
																						current_sub_tokens.append(a		)
																						__UpperCamelCase      :  Dict       =   False
								out_string += self.sp_model.decode(a		)

								return out_string







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					Dict[str, int]:
								__UpperCamelCase      :  Tuple       =   {self.convert_ids_to_tokens(a		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def   _lowerCamelCase							(   self						:Any	,  a						:str	,  a						:Optional[str] = None		)						->					Tuple[str]:
								if not os.path.isdir(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return
								__UpperCamelCase      :  int       =   os.path.join(
								    a	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(a		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file	,  a		)
								elif not os.path.isfile(self.vocab_file		):
															with open(a	,  "wb"		) as fi:
																						__UpperCamelCase      :  Dict       =   self.sp_model.serialized_model_proto()
																						fi.write(a		)

								return (out_vocab_file,)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, List[str]]	,  a						:Union[str, bool] = False		)						->					Union[List[int], List[List[int]], "torch.Tensor"]:

								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   self.preprocess_text(a		)
															__UpperCamelCase      :  List[str]       =   self.sp_model.encode(a		)
								else:
															__UpperCamelCase      :  List[str]       =   [self.preprocess_text(a		) for t in text]
															__UpperCamelCase      :  str       =   self.sp_model.encode(a		)

								if return_tensors is True or return_tensors == "pt":
															__UpperCamelCase      :  Any       =   torch.tensor(a		)

								return token_ids







	def   _lowerCamelCase							(   self						:Any	,  a						:Union[int, List[int]]		)						->					str:
								return self.sp_model.decode(a		)







	def   _lowerCamelCase							(   self						:Dict	,  a						:"Conversation"		)						->					List[int]:
								__UpperCamelCase      :  Dict       =   [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
								__UpperCamelCase      :  int       =   (
								    f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(a		) + f'{self.bos_token}Bot:'
								)
								return self.encode(text=a		)